{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Pandas - 常用操作\n",
    "* DataFrame性质\n",
    "* DataFrame绘图"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DataFrame 性质"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>close</th>\n",
       "      <th>date</th>\n",
       "      <th>ticker</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>776.60</td>\n",
       "      <td>2015-12-29</td>\n",
       "      <td>GOOG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>771.00</td>\n",
       "      <td>2015-12-30</td>\n",
       "      <td>GOOG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>758.88</td>\n",
       "      <td>2015-12-31</td>\n",
       "      <td>GOOG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>741.84</td>\n",
       "      <td>2016-01-04</td>\n",
       "      <td>GOOG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>108.74</td>\n",
       "      <td>2015-12-29</td>\n",
       "      <td>AAPL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>107.32</td>\n",
       "      <td>2015-12-30</td>\n",
       "      <td>AAPL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>105.26</td>\n",
       "      <td>2015-12-31</td>\n",
       "      <td>AAPL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>105.35</td>\n",
       "      <td>2016-01-04</td>\n",
       "      <td>AAPL</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    close        date ticker\n",
       "0  776.60  2015-12-29   GOOG\n",
       "1  771.00  2015-12-30   GOOG\n",
       "2  758.88  2015-12-31   GOOG\n",
       "3  741.84  2016-01-04   GOOG\n",
       "4  108.74  2015-12-29   AAPL\n",
       "5  107.32  2015-12-30   AAPL\n",
       "6  105.26  2015-12-31   AAPL\n",
       "7  105.35  2016-01-04   AAPL"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv('./data/stock.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['close', 'date', 'ticker']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['close', 'date', 'ticker']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 1, 2, 3, 4, 5, 6, 7]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(df.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 1, 2, 3, 4, 5, 6, 7]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[0, 1, 2, 3, 4, 5, 6, 7], ['close', 'date', 'ticker']]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[i.tolist() for i in df.axes]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[776.6, '2015-12-29', 'GOOG'],\n",
       "       [771.0, '2015-12-30', 'GOOG'],\n",
       "       [758.88, '2015-12-31', 'GOOG'],\n",
       "       [741.84, '2016-01-04', 'GOOG'],\n",
       "       [108.74, '2015-12-29', 'AAPL'],\n",
       "       [107.32, '2015-12-30', 'AAPL'],\n",
       "       [105.26, '2015-12-31', 'AAPL'],\n",
       "       [105.35, '2016-01-04', 'AAPL']], dtype=object)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8, 3)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.ndim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>close</th>\n",
       "      <th>date</th>\n",
       "      <th>ticker</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>776.60</td>\n",
       "      <td>2015-12-29</td>\n",
       "      <td>GOOG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>771.00</td>\n",
       "      <td>2015-12-30</td>\n",
       "      <td>GOOG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>758.88</td>\n",
       "      <td>2015-12-31</td>\n",
       "      <td>GOOG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>741.84</td>\n",
       "      <td>2016-01-04</td>\n",
       "      <td>GOOG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>108.74</td>\n",
       "      <td>2015-12-29</td>\n",
       "      <td>AAPL</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    close        date ticker\n",
       "0  776.60  2015-12-29   GOOG\n",
       "1  771.00  2015-12-30   GOOG\n",
       "2  758.88  2015-12-31   GOOG\n",
       "3  741.84  2016-01-04   GOOG\n",
       "4  108.74  2015-12-29   AAPL"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>close</th>\n",
       "      <th>date</th>\n",
       "      <th>ticker</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>741.84</td>\n",
       "      <td>2016-01-04</td>\n",
       "      <td>GOOG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>108.74</td>\n",
       "      <td>2015-12-29</td>\n",
       "      <td>AAPL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>107.32</td>\n",
       "      <td>2015-12-30</td>\n",
       "      <td>AAPL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>105.26</td>\n",
       "      <td>2015-12-31</td>\n",
       "      <td>AAPL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>105.35</td>\n",
       "      <td>2016-01-04</td>\n",
       "      <td>AAPL</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    close        date ticker\n",
       "3  741.84  2016-01-04   GOOG\n",
       "4  108.74  2015-12-29   AAPL\n",
       "5  107.32  2015-12-30   AAPL\n",
       "6  105.26  2015-12-31   AAPL\n",
       "7  105.35  2016-01-04   AAPL"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['GOOG', 'AAPL'], dtype=object)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['ticker'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['ticker'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GOOG    4\n",
       "AAPL    4\n",
       "Name: ticker, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['ticker'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['2015-12-29', '2015-12-30', '2015-12-31', '2016-01-04'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['date'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['date'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2015-12-29    2\n",
       "2016-01-04    2\n",
       "2015-12-30    2\n",
       "2015-12-31    2\n",
       "Name: date, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['date'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count      8.000000\n",
       "mean     434.373750\n",
       "std      350.479228\n",
       "min      105.260000\n",
       "25%      106.827500\n",
       "50%      425.290000\n",
       "75%      761.910000\n",
       "max      776.600000\n",
       "Name: close, dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['close'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 8 entries, 0 to 7\n",
      "Data columns (total 3 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   close   8 non-null      float64\n",
      " 1   date    8 non-null      object \n",
      " 2   ticker  8 non-null      object \n",
      "dtypes: float64(1), object(2)\n",
      "memory usage: 320.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    776.60\n",
       "1    771.00\n",
       "2    758.88\n",
       "Name: close, dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['close'].nlargest(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6    105.26\n",
       "7    105.35\n",
       "5    107.32\n",
       "Name: close, dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['close'].nsmallest(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>close</th>\n",
       "      <th>date</th>\n",
       "      <th>ticker</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>776.60</td>\n",
       "      <td>2015-12-29</td>\n",
       "      <td>GOOG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>771.00</td>\n",
       "      <td>2015-12-30</td>\n",
       "      <td>GOOG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>758.88</td>\n",
       "      <td>2015-12-31</td>\n",
       "      <td>GOOG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>741.84</td>\n",
       "      <td>2016-01-04</td>\n",
       "      <td>GOOG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>108.74</td>\n",
       "      <td>2015-12-29</td>\n",
       "      <td>AAPL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>107.32</td>\n",
       "      <td>2015-12-30</td>\n",
       "      <td>AAPL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>105.26</td>\n",
       "      <td>2015-12-31</td>\n",
       "      <td>AAPL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>105.35</td>\n",
       "      <td>2016-01-04</td>\n",
       "      <td>AAPL</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    close        date ticker\n",
       "0  776.60  2015-12-29   GOOG\n",
       "1  771.00  2015-12-30   GOOG\n",
       "2  758.88  2015-12-31   GOOG\n",
       "3  741.84  2016-01-04   GOOG\n",
       "4  108.74  2015-12-29   AAPL\n",
       "5  107.32  2015-12-30   AAPL\n",
       "6  105.26  2015-12-31   AAPL\n",
       "7  105.35  2016-01-04   AAPL"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.0\n",
       "1    2.0\n",
       "2    3.0\n",
       "3    4.0\n",
       "4    5.0\n",
       "5    6.0\n",
       "6    8.0\n",
       "7    7.0\n",
       "Name: close, dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['close'].rank(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>close</th>\n",
       "      <th>date</th>\n",
       "      <th>ticker</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>776.60</td>\n",
       "      <td>2015-12-29</td>\n",
       "      <td>GOOG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>776.60</td>\n",
       "      <td>2015-12-30</td>\n",
       "      <td>GOOG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>758.88</td>\n",
       "      <td>2015-12-31</td>\n",
       "      <td>GOOG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>741.84</td>\n",
       "      <td>2016-01-04</td>\n",
       "      <td>GOOG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>108.74</td>\n",
       "      <td>2015-12-29</td>\n",
       "      <td>AAPL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>107.32</td>\n",
       "      <td>2015-12-30</td>\n",
       "      <td>AAPL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>105.26</td>\n",
       "      <td>2015-12-31</td>\n",
       "      <td>AAPL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>105.26</td>\n",
       "      <td>2016-01-04</td>\n",
       "      <td>AAPL</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    close        date ticker\n",
       "0  776.60  2015-12-29   GOOG\n",
       "1  776.60  2015-12-30   GOOG\n",
       "2  758.88  2015-12-31   GOOG\n",
       "3  741.84  2016-01-04   GOOG\n",
       "4  108.74  2015-12-29   AAPL\n",
       "5  107.32  2015-12-30   AAPL\n",
       "6  105.26  2015-12-31   AAPL\n",
       "7  105.26  2016-01-04   AAPL"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['close']=[776.6, 776.6, 758.88, 741.84, 108.74, 107.32, 105.26, 105.26]\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    6.0\n",
       "1    6.0\n",
       "2    5.0\n",
       "3    4.0\n",
       "4    3.0\n",
       "5    2.0\n",
       "6    1.0\n",
       "7    1.0\n",
       "Name: close, dtype: float64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['close'].rank(method='dense')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DataFrame绘图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting matplotlib\n",
      "  Downloading matplotlib-3.2.2-cp37-cp37m-win_amd64.whl (9.2 MB)\n",
      "Requirement already satisfied: numpy>=1.11 in c:\\users\\yvonn\\anaconda3\\envs\\base (root)\\lib\\site-packages (from matplotlib) (1.19.0)\n",
      "Collecting cycler>=0.10\n",
      "  Downloading cycler-0.10.0-py2.py3-none-any.whl (6.5 kB)\n",
      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in c:\\users\\yvonn\\anaconda3\\envs\\base (root)\\lib\\site-packages (from matplotlib) (2.4.7)\n",
      "Requirement already satisfied: python-dateutil>=2.1 in c:\\users\\yvonn\\anaconda3\\envs\\base (root)\\lib\\site-packages (from matplotlib) (2.8.1)\n",
      "Collecting kiwisolver>=1.0.1\n",
      "  Downloading kiwisolver-1.2.0-cp37-none-win_amd64.whl (57 kB)\n",
      "Requirement already satisfied: six in c:\\users\\yvonn\\anaconda3\\envs\\base (root)\\lib\\site-packages (from cycler>=0.10->matplotlib) (1.15.0)\n",
      "Installing collected packages: cycler, kiwisolver, matplotlib\n",
      "Successfully installed cycler-0.10.0 kiwisolver-1.2.0 matplotlib-3.2.2\n"
     ]
    }
   ],
   "source": [
    "! pip install matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d651710908>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib\n",
    "df.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d654fc3388>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.set_index('date').plot(figsize=(8,3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d655181608>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(x='date',y='close',figsize=(8,3),color='r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d6550a3b88>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.reset_index().plot(x='index',y='close',figsize=(8,3),kind='scatter')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>close</th>\n",
       "      <th>date</th>\n",
       "      <th>ticker</th>\n",
       "      <th>open</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>776.60</td>\n",
       "      <td>2015-12-29</td>\n",
       "      <td>GOOG</td>\n",
       "      <td>177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>776.60</td>\n",
       "      <td>2015-12-30</td>\n",
       "      <td>GOOG</td>\n",
       "      <td>116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>758.88</td>\n",
       "      <td>2015-12-31</td>\n",
       "      <td>GOOG</td>\n",
       "      <td>156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>741.84</td>\n",
       "      <td>2016-01-04</td>\n",
       "      <td>GOOG</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>108.74</td>\n",
       "      <td>2015-12-29</td>\n",
       "      <td>AAPL</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>107.32</td>\n",
       "      <td>2015-12-30</td>\n",
       "      <td>AAPL</td>\n",
       "      <td>196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>105.26</td>\n",
       "      <td>2015-12-31</td>\n",
       "      <td>AAPL</td>\n",
       "      <td>151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>105.26</td>\n",
       "      <td>2016-01-04</td>\n",
       "      <td>AAPL</td>\n",
       "      <td>113</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    close        date ticker  open\n",
       "0  776.60  2015-12-29   GOOG   177\n",
       "1  776.60  2015-12-30   GOOG   116\n",
       "2  758.88  2015-12-31   GOOG   156\n",
       "3  741.84  2016-01-04   GOOG   168\n",
       "4  108.74  2015-12-29   AAPL   168\n",
       "5  107.32  2015-12-30   AAPL   196\n",
       "6  105.26  2015-12-31   AAPL   151\n",
       "7  105.26  2016-01-04   AAPL   113"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['open']=np.random.randint(100,200,8)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d65520ba08>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d655289348>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(x='date',y=['close','open'],kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d6554afc08>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot('date',[0,-1],color=['r','b'],figsize=(8,3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d6569098c8>"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot('date',['close','open'],figsize=(8,3),kind='bar',ylim=[0,1200],yticks=[0,300,400,500,700],grid=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d656b60f08>"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot('open',figsize=(8,3),kind='hist',bins=[0,105,150,500,700,750,800])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x000001D656D64FC8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001D656E720C8>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAEICAYAAACktLTqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAdWklEQVR4nO3dcbCddZ3f8fdnQ1A2QViMXCAJJK3ZahYWxNuA4tSTndUmEc1uh9akWVjsMlkoOLoD7kTtQN3OtloLUyFoTDWNtCGZ7UBMSqLIdD0G64AEGggxoNkQyyVZUxK8cIFKr377x/OEPR6ec85zc59zzs3vfl4zZ/Kc3+/3PM/3/M73fPPc5z7nPooIzMwsLb/R7wDMzKx6Lu5mZglycTczS5CLu5lZglzczcwS5OJuZpYgF/c+kXS1pO/3Ow4zS5OLu5lZglzczcwS5OLeA5JmS7pX0v+RdETS6oIx75X0iKTh/N/3NvRdLWm/pJckPSNpRUPfv5C0V9ILku6XdF6vXpdZO5LeKaku6eeS9kj6SN6+XtIaSQ/kOf29xryV9I6876ikpyX9s4a+9ZLulLQtX/dhSX+/H69vonNx7zJJU4D7gJ8Cc4CZwKamMWcA24DbgbcCtwHbJL1V0rS8fXFEnAq8F9iVr/cHwGeAfwK8DXgQ2Nj9V2XWnqSpwH8HvgOcCXwc2CDpH+RDVgD/BphBls8b8vWmAQ8Ad+frLQe+LOl3Gja/HPgc8FvAPuAvu/16TkQu7t23ADgH+FREvBwR/zcimn+R+iHgJxHxXyJiNCI2Ak8BH877fwWcL+mUiDgUEXvy9j8F/l1E7I2IUeDfAhf56N0mgEuB6cDnI+K1iPhrsoOc5Xn/tojYERG/AD4LvEfSbOBy4EBE/Of8s/AYcA9wRcO2742IH+Y5vwG4qFcv6kTi4t59s4Gf5onYyjlkR/aNfgrMjIiXgY8C1wKH8h9H35GPOQ/4Uv5j78+Bo4DIfjow66dzgGcj4lcNbT/l73Lz2WONETFClrvnkOX0JcdyOs/rFcBZDdv524blV8j+E7EmLu7d9yxwrqST2ow5SJbUjc4FngOIiPsj4gPA2WRH9P+pYdt/GhGnNzxOiYgfVPsSzMbsIDBbUmONeT2nyQ56AJA0HTgjX+dZ4HtNOT09Iq7rVeCpcHHvvh8Ch4DPS5om6c2SLmsasx34bUn/XNJJkj4KzAfukzQg6SP5uchfACPAL/P11gCfPnY+UtJpkv5pT16VWXsPAy8Dfy5pqqQa2WnGY79vWiLpfZJOJjv3/nBEPEt26ua3JV2ZrzdV0j+U9M5+vIgTmYt7l0XEL8mS+u3A/waGyE6zNI45Qnau8UbgCPDnwOUR8TzZe3Qj2VHNUeD9wL/M19sMfAHYJOlF4ElgcfdflVl7EfEa8BGyfHwe+DJwVUQ8lQ+5G7iFLKffTXbqhYh4CfggsIws5/+WLMff1Mv4UyDfrMPMeknSemAoIv5Vv2NJmY/czcwS5OJuZpYgn5YxM0uQj9zNzBLU7trrrpoxY0bMmTOnsO/ll19m2rRpvQ1oAvI8ZNrNw6OPPvp8RLytxyEdF+d8Z56HTBU537fiPmfOHHbu3FnYV6/XqdVqvQ1oAvI8ZNrNg6Tmb/ZOWM75zjwPmSpy3qdlzMwS5OJuZpYgF3czswS5uJuZJcjF3cwsQS7uZmYJKl3cJU2R9L8k3VfQJ0m3S9on6QlJF1cbpln18nvbfje/B+0eSZ8oGNMytyUtyu/xuU/Sqt5Gb9beWI7cPwHsbdG3GJiXP1YCXxlnXGa9MArcGBHvJLst3PWS5jeNKczt/N64d+b984HlBeua9U2p4i5pFtl9Pr/WYshS4K7IPAScLunsimI064r8frSP5csvkR28NN+isFVuLwD2RcT+/G+Xb8rHmk0IZb+h+h/JbiBxaov+mTTcE5HshhQzye5A9DpJK8mOfhgYGKBerxdubGRkpGXfZHL46DB3bNhS2HfBzNN6HE3/9CIfJM0B3kV2B6FGrXK7qP2Sgu0658eg3Tzsfm64sL3qz0Kr/XRSZRxV5EPH4i7pcuBwRDya3yqrcFhB2xv+3GRErAXWAgwODkarr9f6K8iZOzZs4dbdxW/RgRW13gbTR93Oh/wenvcAn4yIF5u7C1aJNu2/3uCcH5N283D1qm2F7VV/Flrtp5Mq46giH8ocuV8GfETSEuDNwFsk/deI+KOGMUM03PAWmEV2iyyzCU3SVLLCviEi7i0Y0iq3T27RbjYhdDznHhGfjohZETGH7L6Gf91U2AG2AlflVxZcCgxHxKHmbZlNJJIEfB3YGxG3tRjWKrcfAeZJmpvf5HlZPtZsQjjuvwop6VqAiFgDbAeWAPuAV4CPVRKdWXddBlwJ7Ja0K2/7DHAutM/tiBiVdANwPzAFWBcRe3obvllrYyruEVEH6vnymob2AK6vMjCzbouI71N87rxxTMvcjojtZMXfbMLxN1TNzBLk4m5mliAXdzOzBLm4m5klyMXdzCxBLu5mZglycTczS5CLu5lZglzczcwS5OJuZpYgF3czswS5uJuZJcjF3cwsQS7uZmYJcnE3M0uQi7uZWYI6FndJb5b0Q0mPS9oj6XMFY2qShiXtyh83dydcs+pIWifpsKQnW/R/qiGnn5T0S0ln5H0HJO3O+3b2NnKzzsrciekXwO9FxEh+M+HvS/pWRDzUNO7BiLi8+hDNumY9sBq4q6gzIr4IfBFA0oeBP4uIow1DFkbE890O0ux4lLlBdkTESP50av6IrkZl1gMRsQM42nFgZjmwsYvhmFWq1Dl3SVPyGwgfBh6IiIcLhr0nP3XzLUm/U2mUZn0k6TeBRcA9Dc0BfEfSo5JW9icys9aU3f+35GDpdGAz8PGIeLKh/S3Ar/JTN0uAL0XEvIL1VwIrAQYGBt69adOmwv2MjIwwffr0Mb2QFB0+OszPXi3uu2Dmab0Npo/a5cPChQsfjYjB4922pDnAfRFxfpsxHwX+KCI+3NB2TkQclHQm8ADZZ2JHwbrO+TFoNw+7nxsubK/6s9BqP51UGUcVOT+m4g4g6Rbg5Yj4D23GHAAG252PHBwcjJ07i38PVa/XqdVqY4orRXds2MKtu4t/LXLg8x/qcTT90y4fJPWiuG8G/ltE3N2i/18DI+0+E+CcL6PdPMxZta2wverPQqv9dFJlHFXkfJmrZd6WH7Ej6RTg94GnmsacJUn58oJ8u0c6bdtsopN0GvB+YEtD2zRJpx5bBj4IFF5xY9YvZa6WORv4hqQpZEX7ryLiPknXAkTEGuAK4DpJo8CrwLIY648EZj0maSNQA2ZIGgJuIbtg4FheA/wh8J2IeLlh1QFgc348cxJwd0R8u1dxm5XRsbhHxBPAuwra1zQsrya7pMzshBERy0uMWU92yWRj237gwu5EZVYNf0PVzCxBLu5mZglycTczS5CLu5lZglzczcwS5OJuZpYgF3czswS5uJuZJcjF3cwsQS7uZmYJcnE3M0uQi7uZWYJc3M3MEuTibmaWIBd3M7MEubibmSXIxd3MLEFl7qH6Zkk/lPS4pD2SPlcwRpJul7RP0hOSLu5OuGbVkbRO0mFJhfc/lVSTNCxpV/64uaFvkaSn85xf1buozcopc+T+C+D3IuJC4CJgkaRLm8YsBublj5XAVyqN0qw71gOLOox5MCIuyh9/AZDfT/hOsryfDyyXNL+rkZqNUcfiHpmR/OnU/NF88+ulwF352IeA0yWdXW2oZtWKiB3A0eNYdQGwLyL2R8RrwCayz4DZhNHxBtnw+pHKo8DbgTsj4uGmITOBZxueD+Vth5q2s5LsyJ6BgQHq9Xrh/kZGRlr2TSYDp8CNF4wW9k2m+elzPrxH0uPAQeCmiNhDcb5fUrSyc35s2s1Drz4LrfbTSZVxVJEPpYp7RPwSuEjS6cBmSedHRON5ShWtVrCdtcBagMHBwajVaoX7q9frtOqbTO7YsIVbdxe/RQdW1HobTB/1MR8eA86LiBFJS4Bvkp16LJXv4Jwfq3bzcPWqbYXtVX8WWu2nkyrjqCIfxnS1TET8HKjzxvOUQ8DshuezyI50zE5YEfHisVOSEbEdmCppBs53OwGUuVrmbfkRO5JOAX4feKpp2FbgqvyqmUuB4Yg4hNkJTNJZkpQvLyD7vBwBHgHmSZor6WRgGdlnwGzCKHNa5mzgG/l5998A/ioi7pN0LUBErAG2A0uAfcArwMe6FK9ZZSRtBGrADElDwC1kFwwcy+srgOskjQKvAssiIoBRSTcA9wNTgHX5uXizCaNjcY+IJ4B3FbSvaVgO4PpqQzPrrohY3qF/NbC6Rd92soMaswnJ31A1M0uQi7uZWYJc3M3MEuTibmaWIBd3M7MEubibmSXIxd3MLEEu7mZmCXJxNzNLkIu7mVmCXNzNzBLk4m5mliAXdzOzBLm4m5klyMXdzCxBLu5mZglycTczS1CZe6jOlvRdSXsl7ZH0iYIxNUnDknblj5u7E65ZdSStk3RY0pMt+ldIeiJ//EDShQ19ByTtzvN9Z++iNiunzD1UR4EbI+IxSacCj0p6ICJ+1DTuwYi4vPoQzbpmPdlt9O5q0f8M8P6IeEHSYmAtcElD/8KIeL67IZodn45H7hFxKCIey5dfAvYCM7sdmFm3RcQO4Gib/h9ExAv504eAWT0JzKwCyu5tXXKwNAfYAZwfES82tNeAe4Ah4CBwU9Hd4CWtBFYCDAwMvHvTpk2F+xkZGWH69Oml40rV4aPD/OzV4r4LZp7W22D6qF0+LFy48NGIGDzebec5fV9EnN9h3E3AOyLimvz5M8ALQABfjYi1LdZzzo9Bu3nY/dxwYXvVn4VW++mkyjiqyPnSxV3SdOB7wF9GxL1NfW8BfhURI5KWAF+KiHnttjc4OBg7dxafqqzX69RqtVJxpeyODVu4dXfxmbMDn/9Qj6Ppn3b5IKnrxV3SQuDLwPsi4kjedk5EHJR0JvAA8PH8J4GWnPOdtZuHOau2FbZX/VlotZ9OqoyjipwvdbWMpKlkR+Ybmgs7QES8GBEj+fJ2YKqkGWW2bTaRSfpd4GvA0mOFHSAiDub/HgY2Awv6E6FZsTJXywj4OrA3Im5rMeasfBySFuTbPVI01uxEIelc4F7gyoj4cUP7tPziAiRNAz4IFF5xY9YvZa6WuQy4EtgtaVfe9hngXICIWANcAVwnaRR4FVgWYzmZb9YHkjYCNWCGpCHgFmAqvJ7XNwNvBb6cH7uM5j8ODwCb87aTgLsj4ts9fwFmbXQs7hHxfUAdxqwmu6TM7IQREcs79F8DXFPQvh+48I1rmE0c/oaqmVmCXNzNzBLk4m5mliAXdzOzBLm4m5klyMXdzCxBLu5mZglycTczS5CLu5lZglzczcwS5OJuZpYgF3czswS5uJuZJcjF3cwsQS7uZmYJcnE3M0uQi7uZWYLK3EN1tqTvStoraY+kTxSMkaTbJe2T9ISki7sTrll1JK2TdFhS4f1P2+W1pEWSns77VvUuarNyyhy5jwI3RsQ7gUuB6yXNbxqzGJiXP1YCX6k0SrPuWA8satNfmNeSpgB35v3zgeUFnwmzvupY3CPiUEQ8li+/BOwFZjYNWwrcFZmHgNMlnV15tGYViogdwNE2Q1rl9QJgX0Tsj4jXgE35WLMJo+MNshtJmgO8C3i4qWsm8GzD86G87VDT+ivJjoAYGBigXq8X7mdkZKRl32QycArceMFoYd9kmp8+5kOrvC5qv6RoA875sWk3D736LLTaTyd3bNhS2H7BzNNarrP7ueHC9rmnTRn36ypd3CVNB+4BPhkRLzZ3F6wSb2iIWAusBRgcHIxarVa4r3q9Tqu+yeSODVu4dXfxW3RgRa23wfRRH/OhVV6Xyndwzo9Vu3m4etW2wvaqPwut9nO82sXXal/rF00bdz6UKu6SppIV9g0RcW/BkCFgdsPzWcDBcUVm1n+t8vrkFu1mE0aZq2UEfB3YGxG3tRi2Fbgqv7rgUmA4Ig61GGt2omiV148A8yTNlXQysCwfazZhlDlyvwy4EtgtaVfe9hngXICIWANsB5YA+4BXgI9VH6pZtSRtBGrADElDwC3AVGif1xExKukG4H5gCrAuIvb0/AWYtdGxuEfE9yk+x9g4JoDrqwrKrBciYnmH/pZ5HRHbyYq/2YTkb6iamSXIxd3MLEEu7mZmCXJxNzNLkIu7mVmCXNzNzBLk4m5mliAXdzOzBLm4m5klyMXdzCxBLu5mZglycTczS5CLu5lZglzczcwS5OJuZpYgF3czswS5uJuZJajMPVTXSTos6ckW/TVJw5J25Y+bqw/TrDskLZL0tKR9klYV9H+qIbeflPRLSWfkfQck7c77dvY+erPWytxDdT2wGrirzZgHI+LySiIy6xFJU4A7gQ8AQ8AjkrZGxI+OjYmILwJfzMd/GPiziDjasJmFEfF8D8M2K6XjkXtE7ACOdhpndgJaAOyLiP0R8RqwCVjaZvxyYGNPIjMbJ2X3AO4wSJoD3BcR5xf01YB7yI58DgI3tboTvKSVwEqAgYGBd2/atKlwfyMjI0yfPr3UC0jZ4aPD/OzV4r4LZp7W22D6qF0+LFy48NGIGDye7Uq6AlgUEdfkz68ELomIGwrG/iZZjr/92JG7pGeAF4AAvhoRawvWc86PQbt52P3ccGF71Z+FVvs5Xu3ia7WvuadNGXfOlzkt08ljwHkRMSJpCfBNYF7RwDz51wIMDg5GrVYr3GC9XqdV32Ryx4Yt3Lq7+C06sKLW22D6qIv5oIK2Vkc7Hwb+Z9Mpmcsi4qCkM4EHJD2V/6T7dxtzzo9Ju3m4etW2wvaqPwut9nO82sXXal/rF00bdz6M+2qZiHgxIkby5e3AVEkzxrtdsx4YAmY3PJ9F9tNnkWU0nZKJiIP5v4eBzWSnecwmhHEXd0lnSVK+vCDf5pHxbtesBx4B5kmaK+lksgK+tXmQpNOA9wNbGtqmSTr12DLwQaDwijKzfuh4WkbSRqAGzJA0BNwCTAWIiDXAFcB1kkaBV4FlUeZEvlmfRcSopBuA+4EpwLqI2CPp2rx/TT70D4HvRMTLDasPAJvz45qTgLsj4tu9i96svY7FPSKWd+hfTXappNkJJz+VuL2pbU3T8/VklwQ3tu0HLuxyeGbHzd9QNTNLkIu7mVmCXNzNzBLk4m5mliAXdzOzBLm4m5klyMXdzCxBLu5mZglycTczS5CLu5lZglzczcwS5OJuZpYgF3czswS5uJuZJcjF3cwsQS7uZmYJcnE3M0tQx+IuaZ2kw5IK7w+pzO2S9kl6QtLF1Ydp1h2SFkl6Os/fVQX9NUnDknblj5vLrmvWT2WO3NcDi9r0Lwbm5Y+VwFfGH5ZZ90maAtxJlsPzgeWS5hcMfTAiLsoffzHGdc36omNxj4gdwNE2Q5YCd0XmIeB0SWdXFaBZFy0A9kXE/oh4DdhEls/dXtes6zreILuEmcCzDc+H8rZDzQMlrSQ7umdgYIB6vV64wZGRkZZ9k8nAKXDjBaOFfZNpfrqYD0W5e0nBuPdIehw4CNwUEXvKruucH5t289Crz0Kr/RyvdvG12lcV+VBFcVdBWxQNjIi1wFqAwcHBqNVqhRus1+u06ptM7tiwhVt3F79FB1bUehtMH3UxH8rk7mPAeRExImkJ8E2yU5Cl8t45Pzbt5uHqVdsK26v+LLTaz/FqF1+rfa1fNG3c+VDF1TJDwOyG57PIjnDMJrqOuRsRL0bESL68HZgqaUaZdc36qYrivhW4Kr9q5lJgOCLecErGbAJ6BJgnaa6kk4FlZPn8OklnSVK+vIDsM3OkzLpm/dTxtIykjUANmCFpCLgFmAoQEWuA7cASYB/wCvCxbgVrVqWIGJV0A3A/MAVYFxF7JF2b968BrgCukzQKvAosi4gACtftywsxK9CxuEfE8g79AVxfWURmPZSfatne1LamYXk1sLrsumYThb+hamaWIBd3M7MEubibmSXIxd3MLEEu7mZmCXJxNzNLkIu7mVmCXNzNzBLk4m5mliAXdzOzBLm4m5klyMXdzCxBLu5mZglycTczS5CLu5lZglzczcwS5OJuZpagUsVd0iJJT0vaJ2lVQX9N0rCkXfnj5upDNateidxeIemJ/PEDSRc29B2QtDvP+Z29jdysvTL3UJ0C3Al8gOyO749I2hoRP2oa+mBEXN6FGM26omRuPwO8PyJekLQYWAtc0tC/MCKe71nQZiWVOXJfAOyLiP0R8RqwCVja3bDMeqJjbkfEDyLihfzpQ8CsHsdodlw6HrkDM4FnG54P8etHLse8R9LjwEHgpqI7wUtaCawEGBgYoF6vF+5wZGSkZd9kMnAK3HjBaGHfZJqfLuZD2dw+5k+AbzU8D+A7kgL4akSsbV7BOT827eahV5+FVvs5Xu3ia7WvKvKhTHFXQVs0PX8MOC8iRiQtAb4JzHvDSlnyrwUYHByMWq1WuMN6vU6rvsnkjg1buHV38Vt0YEWtt8H0URfzoUxuZwOlhWTF/X0NzZdFxEFJZwIPSHoqInb82sac82PSbh6uXrWtsL3qz0Kr/RyvdvG12tf6RdPGnQ9lTssMAbMbns8iOzp/XUS8GBEj+fJ2YKqkGeOKzKz7OuY2gKTfBb4GLI2II8faI+Jg/u9hYDPZaR6zCaFMcX8EmCdprqSTgWXA1sYBks6SpHx5Qb7dI2/YktnEUia3zwXuBa6MiB83tE+TdOqxZeCDwJM9i9ysg46nZSJiVNINwP3AFGBdROyRdG3evwa4ArhO0ijwKrAsIgp/vDWbKErm9s3AW4Ev58cvoxExCAwAm/O2k4C7I+LbfXgZZoXKnHM/dqple1Pbmobl1cDqakMz674SuX0NcE3BevuBC5vbzSYKf0PVzCxBLu5mZglycTczS5CLu5lZglzczcwS5OJuZpYgF3czswS5uJuZJcjF3cwsQS7uZmYJcnE3M0uQi7uZWYJc3M3MEuTibmaWIBd3M7MEubibmSXIxd3MLEGlirukRZKelrRP0qqCfkm6Pe9/QtLF1YdqVr3x5Handc36qWNxlzQFuBNYDMwHlkua3zRsMTAvf6wEvlJxnGaVG09ul1zXrG/KHLkvAPZFxP6IeA3YBCxtGrMUuCsyDwGnSzq74ljNqjae3C6zrlnflLlB9kzg2YbnQ8AlJcbMBA41DpK0kuzoB2BE0tMt9jkDeL5EbKlrOQ/6Qo8j6a92+XDeOLY7ntwus65zfuzGPA8T/bNwPPEt/ML4c75McVdBWxzHGCJiLbC24w6lnRExWCK2pHkeMl2ch/HktnO+CzwPmSrmoUxxHwJmNzyfBRw8jjFmE814cvvkEuua9U2Zc+6PAPMkzZV0MrAM2No0ZitwVX5lwaXAcEQcat6Q2QQzntwus65Z33Q8co+IUUk3APcDU4B1EbFH0rV5/xpgO7AE2Ae8AnxsnHF1/DF2kvA8ZLoyD+PJ7VbrjiMcv9cZz0Nm3POgiDecJjQzsxOcv6FqZpYgF3czswRNqOI+2b7OLWmdpMOSnmxoO0PSA5J+kv/7Ww19n87n5mlJ/7g/UVdL0mxJ35W0V9IeSZ/I2yfFPDjnJ8973agneR8RE+JB9kupvwH+HtllZo8D8/sdV5df8z8CLgaebGj798CqfHkV8IV8eX4+J28C5uZzNaXfr6GCOTgbuDhfPhX4cf5ak58H5/zrbcm/1wXz0PW8n0hH7pPu69wRsQM42tS8FPhGvvwN4A8a2jdFxC8i4hmyqzcW9CTQLoqIQxHxWL78ErCX7Nufk2EenPOZyfBe/5pe5P1EKu6tvuY92QxE/h2B/N8z8/bk50fSHOBdwMNMjnlI6bWMx2R4r1vqVt5PpOJe6uvck1jS8yNpOnAP8MmIeLHd0IK2E3UeUnot3ZD8/HQz7ydScfefMMj87Nhf1Mz/PZy3Jzs/kqaSJfiGiLg3b54M85DSaxmPyfBev0G3834iFXd/nTuzFfjjfPmPgS0N7cskvUnSXLK/L/7DPsRXKUkCvg7sjYjbGromwzw45zOT4b3+NT3J+37/1rjpN8hLyH5r/DfAZ/sdTw9e70ayP4v8/8j+Z/4T4K3A/wB+kv97RsP4z+Zz8zSwuN/xVzQH7yP78fIJYFf+WDJZ5sE5P/lyPn9dXc97//kBM7METaTTMmZmVhEXdzOzBLm4m5klyMXdzCxBLu5mZglycTczS5CLu5lZgv4/b89lEBB65mQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hist(bins=20,range=[0,200])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d657226d08>"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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qdL1eZ3R0tKtt+2k+17GX5wDYds3aASd60+z5Oc5dGHSKS42txlwdMFdnhjXXlrUjXXfYxMTEkcwcb7au5SkXIJosW/xb4Hng3ZlZj4idwB8CWy/ZKHMvsBdgfHw8a7VaG7u/1PT0NN1u20/zuT5+7zcBOP2R2mADLfDw/gM8eKydH/fK2r3tork6YK7ODGuufTvW9KXD2jnlcgbYtGB+I42j8H+Wma9lZr2aPgysioj1PUspSWqpnUL/PrA1IrZExFuBXcDBhQMi4l0REdX09dXzvtrrsJKkpbV8LZKZFyPiU8C3gRHgscw8ERF3Vev3ALcDn4yIi8AFYFe2OjkvSeqptk4uVadRDi9atmfB9CPAI72NJknqhO8UlaRCWOiSVAgLXZIKYaFLUiEsdEkqhIUuSYWw0CWpEBa6JBXCQpekQljoklQIC12SCmGhS1IhLHRJKoSFLkmFsNAlqRAWuiQVwkKXpEK0VegRsSMiXoqIUxFxb5P1EREPVetfjIjreh9VkrScloUeESPAl4BbgPcCH46I9y4adguwtXpMAo/2OKckqYV2jtCvB05l5p9n5j8AU8Bti8bcBjyeDc8B6yJiQ4+zSpKWEZm5/ICI24EdmfmJav5jwA2Z+akFYw4BD2Tms9X808A9mTmz6LkmaRzBA7wHeKnL3OuBH3e5bT8Nay4Y3mzm6oy5OlNirndn5tXNVryljY2jybLFvwXaGUNm7gX2trHP5QNFzGTm+OU+T68Nay4Y3mzm6oy5OvOvLVc7p1zOAJsWzG8EznYxRpLUR+0U+veBrRGxJSLeCuwCDi4acxC4o7ra5UZgLjNf6XFWSdIyWp5yycyLEfEp4NvACPBYZp6IiLuq9XuAw8BO4BTwBnBn/yIDPTht0yfDmguGN5u5OmOuzvyrytXyj6KSpCuD7xSVpEJY6JJUiCuu0Ft9DEGf9/1YRMxGxPEFy94ZEU9FxJ9WX39mwbr7qpwvRcR/6mOuTRHxTEScjIgTEfHpYcgWEW+LiO9FxAtVrs8PQ65qPyMR8YPqPRRDkana1+mIOBYRRyNiZliyRcS6iPhaRPyo+nf2gUHnioj3VN+n+cdrEfGZQeeq9vNfq3/zxyPiier/Qv9zZeYV86DxR9k/A34BeCvwAvDeFdz/LwHXAccXLPsd4N5q+l7gv1fT763y/TSwpco90qdcG4DrqumrgP9X7X+g2Wi8P2G0ml4F/Alw46BzVfv6deB/AYeG5edY7e80sH7RsoFnA74CfKKafiuwbhhyLcg3Avw18O5B5wKuAf4CWF3NfxX4+Erk6ts3uE8/tA8A314wfx9w3wpn2My/LPSXgA3V9AbgpWbZaFwl9IEVyngA+JVhyga8HXgeuGHQuWi8T+Jp4GbeLPSh+F7RvNAH/f16R1VQMUy5FmX5j8D/HYZcNAr9r4B30riS8FCVr++5rrRTLvPfqHlnqmWDNJbVNffV15+tlg8ka0RsBt5P42h44NmqUxtHgVngqcwchly/B/wG8JMFywadaV4CfxQRR6LxURnDkO0XgL8B/kd1mur3I2LNEORaaBfwRDU90FyZ+TLwu8BfAq/QeF/OH61Eriut0Nv6iIEhseJZI2IUeBL4TGa+ttzQJsv6ki0z/ykz30fjqPj6iLh2kLki4lZgNjOPtLtJk2X9/DnelJnX0fgE07sj4peWGbtS2d5C41Tjo5n5fuB1GqcMBp2rsbPGGx4/BPzvVkObLOt5rurc+G00Tp/8HLAmIj66ErmutEIfxo8YOBfVJ0tWX2er5SuaNSJW0Sjz/Zn59WHKBpCZfwtMAzsGnOsm4EMRcZrGJ4feHBF/MOBM/ywzz1ZfZ4Fv0Pi000FnOwOcqV5dAXyNRsEPOte8W4DnM/NcNT/oXB8E/iIz/yYz/xH4OvAfViLXlVbo7XwMwUo7CPxaNf1rNM5fzy/fFRE/HRFbaHxW/Pf6ESAiAvgycDIzvzAs2SLi6ohYV02vpvEP/UeDzJWZ92XmxszcTOPfzx9n5kcHmWleRKyJiKvmp2mcdz0+6GyZ+dfAX0XEe6pFvwz8cNC5Fvgwb55umd//IHP9JXBjRLy9+r/5y8DJFcnVzz9U9OmPHztpXMXxZ8BvrfC+n6BxTuwfafxW/c/Av6HxB7Y/rb6+c8H436pyvgTc0sdcv0jjJdqLwNHqsXPQ2YB/D/ygynUcuL9aPvDvWbWvGm/+UXTgmWicq36hepyY//c9JNneB8xUP8s/BH5mSHK9HXgVWLtg2TDk+jyNg5fjwP+kcQVL33P51n9JKsSVdspFkrQEC12SCmGhS1IhLHRJKoSFLkmFsNAlqRAWuiQV4v8DdgyF3xdFQ8kAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['close'].hist(bins=[0,105,106,107,110,200,800],grid=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>close</th>\n",
       "      <th>date</th>\n",
       "      <th>ticker</th>\n",
       "      <th>open</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>776.60</td>\n",
       "      <td>2015-12-29</td>\n",
       "      <td>GOOG</td>\n",
       "      <td>177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>776.60</td>\n",
       "      <td>2015-12-30</td>\n",
       "      <td>GOOG</td>\n",
       "      <td>116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>758.88</td>\n",
       "      <td>2015-12-31</td>\n",
       "      <td>GOOG</td>\n",
       "      <td>156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>741.84</td>\n",
       "      <td>2016-01-04</td>\n",
       "      <td>GOOG</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>108.74</td>\n",
       "      <td>2015-12-29</td>\n",
       "      <td>AAPL</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>107.32</td>\n",
       "      <td>2015-12-30</td>\n",
       "      <td>AAPL</td>\n",
       "      <td>196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>105.26</td>\n",
       "      <td>2015-12-31</td>\n",
       "      <td>AAPL</td>\n",
       "      <td>151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>105.26</td>\n",
       "      <td>2016-01-04</td>\n",
       "      <td>AAPL</td>\n",
       "      <td>113</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    close        date ticker  open\n",
       "0  776.60  2015-12-29   GOOG   177\n",
       "1  776.60  2015-12-30   GOOG   116\n",
       "2  758.88  2015-12-31   GOOG   156\n",
       "3  741.84  2016-01-04   GOOG   168\n",
       "4  108.74  2015-12-29   AAPL   168\n",
       "5  107.32  2015-12-30   AAPL   196\n",
       "6  105.26  2015-12-31   AAPL   151\n",
       "7  105.26  2016-01-04   AAPL   113"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 课后练习"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "创建一个学生成绩的dataframe，数学成绩 student a 86分，student b 90分，student c 75分，student d 100分，\n",
    "\n",
    "语文成绩 student a 100分，student b 60分，student c 85分，student d 98分"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "列出score的column,index和value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "计算数学成绩和语文成绩的分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": false,
   "sideBar": true,
   "skip_h1_title": true,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {
    "height": "calc(100% - 180px)",
    "left": "10px",
    "top": "150px",
    "width": "307px"
   },
   "toc_section_display": true,
   "toc_window_display": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
